Search Results
You are looking at 1 - 10 of 19 items for
- Author or Editor: Karl E. Taylor x
- Refine by Access: All Content x
Abstract
A hierarchy of zonally averaged atmospheric models is used to study the role of mean meridional motions and large-scale eddies in determining the zonal climate. Five models are developed: a radiative-convective equilibrium model (no large-scale motion), a zonally uniform model (no longitudinal asymmetries), an energy balance model (parameterized energy transport), a model that combines the physics of the two previous models, and a full statistical-dynamic model (with parameterizations of eddy momentum transport as well as eddy sensible heat transport).
In the most complete model, the zonally averaged primitive equations are solved after parameterizing the eddies, friction and the diabatic heating rates. All the models have two layers in the vertical and a latitudinal grid resolution of 5°. For simplicity, we treat a “dry earth” case and calculate annual-average equilibrium states with each of the five models.
We find that in the tropics a parameterized energy transport commonly used in energy balance models does not accurately simulate the energy transport as determined by an explicit calculation of the Hadley cell. The explicitly calculated Hadley transport is generally greater than the parameterized transport and leads to very small horizontal temperature gradients in the tropics. The strength of the Hadley cell is determined by both the local heating distribution and extratropical heat and momentum transport by the eddies. The extratropical mean meridional motions are primarily driven by the requirements of the momentum budget. An indirect (Ferrel) cell appears only when eddy momentum transport is included in the model.
Abstract
A hierarchy of zonally averaged atmospheric models is used to study the role of mean meridional motions and large-scale eddies in determining the zonal climate. Five models are developed: a radiative-convective equilibrium model (no large-scale motion), a zonally uniform model (no longitudinal asymmetries), an energy balance model (parameterized energy transport), a model that combines the physics of the two previous models, and a full statistical-dynamic model (with parameterizations of eddy momentum transport as well as eddy sensible heat transport).
In the most complete model, the zonally averaged primitive equations are solved after parameterizing the eddies, friction and the diabatic heating rates. All the models have two layers in the vertical and a latitudinal grid resolution of 5°. For simplicity, we treat a “dry earth” case and calculate annual-average equilibrium states with each of the five models.
We find that in the tropics a parameterized energy transport commonly used in energy balance models does not accurately simulate the energy transport as determined by an explicit calculation of the Hadley cell. The explicitly calculated Hadley transport is generally greater than the parameterized transport and leads to very small horizontal temperature gradients in the tropics. The strength of the Hadley cell is determined by both the local heating distribution and extratropical heat and momentum transport by the eddies. The extratropical mean meridional motions are primarily driven by the requirements of the momentum budget. An indirect (Ferrel) cell appears only when eddy momentum transport is included in the model.
Abstract
The traditional method of estimating cyclone frequency by counting the number of storms passing through latitude-longitude grid cells is known to be biased because it effectively overweights the lower latitudes. Here we show that it is also biased in that it preferentially weights certain storm track directions over others. An analysis of storm tracks affecting a few locations in North America and the North Atlantic shows that these biases can distort the frequency estimates by at least 14%. It is not possible to correct for these biases unless the prevailing storm track direction is measured at each location. Unbiased alternatives are discussed, however, which can be used when better accuracy is required.
Abstract
The traditional method of estimating cyclone frequency by counting the number of storms passing through latitude-longitude grid cells is known to be biased because it effectively overweights the lower latitudes. Here we show that it is also biased in that it preferentially weights certain storm track directions over others. An analysis of storm tracks affecting a few locations in North America and the North Atlantic shows that these biases can distort the frequency estimates by at least 14%. It is not possible to correct for these biases unless the prevailing storm track direction is measured at each location. Unbiased alternatives are discussed, however, which can be used when better accuracy is required.
Abstract
Vertical finite-differences schemes that preserve the important integral properties of the continuum equations are derived for the basic hydrodynamic and thermodynamic equations governing the atmosphere. The vertical coordinate is altitude. The first scheme applies in fully nonhydrostatic cases and therefore can be used to study smaller motion when the hydrostatic approximation cannot be justified. The finite-difference equations are then reduced to a hydrostatic set that continues to satisfy the integral constraints. In this simplified form, the vertical difference scheme guarantees local compliance with hydrostatic balance, in contrast to some sigma-coordinate models in which nonlocal treatments of this approximation may be required.
Abstract
Vertical finite-differences schemes that preserve the important integral properties of the continuum equations are derived for the basic hydrodynamic and thermodynamic equations governing the atmosphere. The vertical coordinate is altitude. The first scheme applies in fully nonhydrostatic cases and therefore can be used to study smaller motion when the hydrostatic approximation cannot be justified. The finite-difference equations are then reduced to a hydrostatic set that continues to satisfy the integral constraints. In this simplified form, the vertical difference scheme guarantees local compliance with hydrostatic balance, in contrast to some sigma-coordinate models in which nonlocal treatments of this approximation may be required.
Abstract
A set of general circulation model simulations is analyzed to determine how cloud distribution and cloud radiative properties might change as climate warms and to isolate and quantify the various feedback effects of clouds on climate sensitivity. For this study the NCAR Community Climate Model (CCM1) was modified so that the cloud radiative properties (albodo, emissivity, and absorptivity) are no longer prescribed, but are functions of the cloud liquid water content. Following the Cess and Potter approach for estimating climate sensitivity, we consider results from two sets of simulations. In one set, cloud liquid water is diagnosed from the simulated condensation rate and thus is free to vary with condensation, while in the other set, the cloud liquid water content is a fixed field (dependent only on altitude and latitude) that is obtained by averaging the results of the first set of experiments. The experiments make it possible to isolate the effects of cloud liquid water feedback.
We find that changes in cloud amount, cloud liquid water content, and cloud distribution (especially in the vertical) are all of comparable importance, but some of these changes provide a positive feedback while others provide a negative feedback. Separation of cloud feedback into individual components makes it clear that in this model as climate warms the general increase in the liquid water content of each cloud layer is partially offset by an upward shift in cloud altitude. The effects of clouds on longwave radiation also generally tend to cancel the effects on shortwave radiation. Consequently, the net cloud feedback represents a residual of several offsetting effects, which nevertheless is large enough to nearly double the sensitivity of the simulated climate. Another important conclusion is that it is impossible to parameterize cloud albedo in terms of average cloud liquid water content because the average liquid water is dominated by the thicker clouds, whereas the average albedo depends on clouds with relatively little liquid water as well.
Abstract
A set of general circulation model simulations is analyzed to determine how cloud distribution and cloud radiative properties might change as climate warms and to isolate and quantify the various feedback effects of clouds on climate sensitivity. For this study the NCAR Community Climate Model (CCM1) was modified so that the cloud radiative properties (albodo, emissivity, and absorptivity) are no longer prescribed, but are functions of the cloud liquid water content. Following the Cess and Potter approach for estimating climate sensitivity, we consider results from two sets of simulations. In one set, cloud liquid water is diagnosed from the simulated condensation rate and thus is free to vary with condensation, while in the other set, the cloud liquid water content is a fixed field (dependent only on altitude and latitude) that is obtained by averaging the results of the first set of experiments. The experiments make it possible to isolate the effects of cloud liquid water feedback.
We find that changes in cloud amount, cloud liquid water content, and cloud distribution (especially in the vertical) are all of comparable importance, but some of these changes provide a positive feedback while others provide a negative feedback. Separation of cloud feedback into individual components makes it clear that in this model as climate warms the general increase in the liquid water content of each cloud layer is partially offset by an upward shift in cloud altitude. The effects of clouds on longwave radiation also generally tend to cancel the effects on shortwave radiation. Consequently, the net cloud feedback represents a residual of several offsetting effects, which nevertheless is large enough to nearly double the sensitivity of the simulated climate. Another important conclusion is that it is impossible to parameterize cloud albedo in terms of average cloud liquid water content because the average liquid water is dominated by the thicker clouds, whereas the average albedo depends on clouds with relatively little liquid water as well.
Abstract
A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback.
These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.
Abstract
A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback.
These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.
The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
Abstract
We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.
Abstract
We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.
Abstract
Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.
Abstract
Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.
Abstract
Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.
Abstract
Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.
